Abstract

This thesis focuses on the development and extension of nonlinear evolutionary model predictive control (NEMPC), a control algorithm previously developed by Phil Hyatt of the BYU RaD Lab. While this controller and its variants are applicable to any high degree-of-freedom (DoF) robotic system, particular emphasis is given in this thesis to control of a soft robot continuum joint. First, speed improvements are presented for NEMPC. Second, a Python package is presented as a companion to NEMPC, as a method of establishing a common interface for dynamic simulators and approximating each system by a deep neural network (DNN). Third, a method of training a DNN approximation of a hardware system that is generalize-able to more complex hardware systems is presented. This method is shown to reduce median tracking error on a soft robot hardware platform by 88%. Finally, particle swarm model predictive control (PSOMPC), a variant of NEMPC, is presented and modified to model and account for uncertainty in a dynamic system. Control performance of NEMPC and PSOMPC are presented for a set of control trials on simulated systems with uncertainty in parameters, states, and inputs, as well as on a soft robot hardware platform. PSOMPC is shown to have an increased robustness to system uncertainty, reducing expected collisions by 71% for a three-link robot arm with parameter uncertainty, input disturbances, and state measurement error.

Degree

MS

College and Department

Mechanical Engineering

Rights

https://lib.byu.edu/about/copyright/

Date Submitted

2021-11-23

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd11922

Keywords

soft robot, uncertainty, control, model predictive control, heuristic algorithm

Language

english

Included in

Engineering Commons

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